Jakub Lengiewicz

Abstract: Over the last decade, the use of drive-by inspection technology for bridge damage assessment has been widely studied by scholars. It consists of identifying bridge damage from the response of an instrumented sensing vehicle. Most current methods are based on identifying bridge properties and supervised learning techniques. However, these approaches require data from the bridge at its different states (i.e., healthy and damaged conditions), which is not always available. Hence, this study proposes a fully unsupervised computer vision-based methodology for bridge structural health monitoring (SHM) based on the time-frequency domain analysis of the acceleration signal recorded by a two-axle vehicle. A convolutional variational autoencoders (CVAE) algorithm is trained only with the Continuous Wavelet Transform (CWT) of vehicle acceleration responses while passing over a bridge at its benchmark state. The damage index is defined from the measured error between the original and the reconstructed CWT images. During testing, the error between the original and the reconstructed CWT is compared with the damage index from the benchmark state to classify the new samples as healthy or damaged. The methodology is tested on a numerical and experimental vehicle-bridge interaction (VBI) model. Different damage types and severities are considered. The effect of road roughness is also studied.

Andres Felipe Calderon Hurtado did his master’s degree in Structural Engineering at UNSW, Australia. Currently, he is a Ph.D. candidate in the department of Civil Engineering at UNSW, working on indirect bridge structural health monitoring. His research interest includes computational mechanics, data-driven methodologies for structural health monitoring, signal processing, and machine learning.